Dataset Information

Novel Transforming Growth Factor-Beta Receptor 1 Antagonists through a Pharmacophore-Based Virtual Screening Approach.

ABSTRACT: As new drugs for the treatment of malignant tumors, transforming growth factor-beta receptor 1 (TGF?R1) antagonists have attracted wide attention. Based on the crystal structure of TGF?R1-BMS22 complex, the pharmacophore model A02 with two hydrogen bond acceptors (HBAs) and four hydrophobic (HYD) properties was constructed. From the common features of active ligands reported in the literature, pharmacophore model B10 was also generated, which has two aromatic ring centers (RAs) and two HYD properties. The two models have high sensitivity and specificity to the training set, and they are highly consistent in spatial structure. Combining the two pharmacophore models, two novel skeleton structures with potential activity were selected by virtual screening from the DruglikeDiverse, MiniMaybridge, and ZINC Drug-Like databases. Four compounds (YXY01?YXY04) with potential anti-TGF?R1 activity were designed based on the new skeleton structures. In combination with Lipinski's rules; absorption, distribution, metabolism, excretion, and toxicity (ADMET); and, toxicological properties predicted in the study, YXY01-03 with the novel skeleton, good drug-like properties, and potential activity were finally discovered and may have higher safety relative to BMS22, which may be valuable for further research.

SUBMITTER: Jiang J

PROVIDER: S-EPMC6278322 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Novel Transforming Growth Factor-Beta Receptor 1 Antagonists through a Pharmacophore-Based Virtual Screening Approach.

Jiang Junhao J Zhou Hui H Jiang Qihua Q Sun Lili L Deng Ping P

Molecules (Basel, Switzerland) 20181031 11

As new drugs for the treatment of malignant tumors, transforming growth factor-beta receptor 1 (TGFβR1) antagonists have attracted wide attention. Based on the crystal structure of TGFβR1-BMS22 complex, the pharmacophore model A02 with two hydrogen bond acceptors (HBAs) and four hydrophobic (HYD) properties was constructed. From the common features of active ligands reported in the literature, pharmacophore model B10 was also generated, which has two aromatic ring centers (RAs) and two HYD prope ...[more]

PMID: 30384428

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Project description:Transforming growth factor-beta (TGF-β), a member of the TGF-β cytokine superfamily, is known to bind to sulfated glycosaminoglycans (GAGs), but the nature of this interaction remains unclear. In a recent study, we found that preterm human milk TGF-β2 is sequestered by chondroitin sulfate (CS) in its proteoglycan form. To understand the molecular basis of the TGF-β2-CS interaction, we utilized the computational combinatorial virtual library screening (CVLS) approach in tandem with molecular dynamics (MD) simulations. All possible CS oligosaccharides were generated in a combinatorial manner to give 24 di- (CS02), 192 tetra- (CS04), and 1536 hexa- (CS06) saccharides. This library of 1752 CS oligosaccharides was first screened against TGF-β2 using the dual filter CVLS algorithm in which the GOLDScore and root-mean-square-difference (RMSD) between the best bound poses were used as surrogate markers for in silico affinity and in silico specificity. CVLS predicted that both the chain length and level of sulfation are critical for the high affinity and high specificity recognition of TGF-β2. Interestingly, CVLS led to identification of two distinct sites of GAG binding on TGF-β2. CVLS also deduced the preferred composition of the high specificity hexasaccharides, which were further assessed in all-atom explicit solvent MD simulations. The MD results confirmed that both sites of binding form stable GAG-protein complexes. More specifically, the highly selective CS chains were found to engage the TGF-β2 monomer with high affinity. Overall, this work present key principles of recognition with regard to the TGF-β2-CS system. In the process, it led to the generation of the in silico library of all possible CS oligosaccharides, which can be used for advanced studies on other protein-CS systems. Finally, the study led to the identification of unique CS sequences that are predicted to selectively recognize TGF-β2 and may out-compete common natural CS biopolymers.

Dataset Information

Novel Transforming Growth Factor-Beta Receptor 1 Antagonists through a Pharmacophore-Based Virtual Screening Approach.

Publications

Novel Transforming Growth Factor-Beta Receptor 1 Antagonists through a Pharmacophore-Based Virtual Screening Approach.

Similar Datasets

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